Session Information
28 SES 08 A, Data Visions: Education in the Age of Digital Data Visualizations (Part 2)
Symposium continued from 28 SES 07 A
Contribution
Dashboards, progression curves, benchmarks, and traffic lights. All are examples of data visualizations used to mobilize data about educational institutions and their students. Data visualizations are graphic representations of digital data which summazise large amounts of data to patterns and trends within data sets. Data visualizations signpost the emergence of educational institutions and students as data objects, which can be observed and compared on a computer screen. They are thus shaping educational administrators’ and teachers’ socio-technical ways of ‘seeing’ educational quality and learning (Selwyn et al., 2022), and it is crucial to investigate their world making capacities and their ‘social life’ in educational worlds. As the main ‘interface’ through which educational administrators and educators access data, they are an underlooked but central aspect of the datafication of education.
This symposium investigates the role of data visualizations as a distinct way of making ‘education’ or ‘learning’ tangible and knowable. Although praised for making data accessible and interpretable, data visualizations also imply a distancing from data. Issues relating to how data is categorized in a database and how statistical techniques are performed on data are not included in visualizations (Ratner & Ruppert, 2019). The software developers of data visualizations make numerous design choices rendering some things absent and others present (Greller & Drachsler, 2012). While visualizations may appear factual and transparent, data visualizations provide neither direct nor neutral access to the object they are deemed to represent. Rather, they may be seen as persuasive and value-laden devices that privilege certain viewpoints (Latour, 1990).
This symposium examines data visualizations as entry point for discussing issues related to power, governance and automation. Dashboards visualizing the performance of educational institutions are today mundane artifacts in educational governance and require actors at different levels of governance hierarchies to turn performance gaps into improved outcomes (Decuypere & Landri, 2021; Ratner & Gad, 2018). Here, visualizations may have an affective dimension with e.g. rankings encouraging a dynamics of faming and shaming (Brøgger, 2016; Sellar, 2015), which, in turn, may situate education in a wider political context of competition and accountability. We may also examine questions of automation through data visualizations. With data visualizations increasingly presenting pre-fabricated interpretations of data, they now conduct some of the professional judgment formerly done by teachers (e.g. identifying low performing students). This may naturalize new forms of knowledge such as ‘at risk students’. It also maps out new responsibilities for teachers, such as ‘acting on’ visualizations to improve student learning. It is thus likely that visualizations both shape what counts as educational quality and signal to administrators and educators what they should prioritize. This raises important questions about how data visualizations reconfigure human judgment and decision-making in a digital and datafied age. While powerful, however, data visualizations can never fully determine the social contexts they are part of. Users may take them up in unanticipated ways. Thus, it is equally important to examine how educators and administrators make sense of data visualizations, ignore them, resist them or put them to other uses than those anticipated by the designers.
This conference symposium will explore the role of data visualizations in education across Europe and beyond. It does so by comparing different European and international cases of how data visualizations are used in education, including historical and contemporary examples. The symposium includes contributions examining both the production and consumption of data visualizations. Across the different contributions, it will also discuss conceptual and methodological questions arising from the study of educational data visualizations.
References
Brøgger, K. (2016). The rule of mimetic desire in higher education: Governing through naming, shaming and faming. British Journal of Sociology of Education, 37(1), 72–91. Decuypere, M., & Landri, P. (2021). Governing by visual shapes: University rankings, digital education platforms and cosmologies of higher education. Critical Studies in Education, 62(1), 17–33. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57. Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in Scientific Practice (pp. 19–68). MIT Press. Ratner, H., & Gad, C. (2018). Data warehousing organization: Infrastructural experimentation with educational governance. Organization, 1350508418808233. Ratner, H., & Ruppert, E. (2019). Producing and projecting data: Aesthetic practices of government data portals. Big Data & Society, 6(2), 2053951719853316. Sellar, S. (2015). A feel for numbers: Affect, data and education policy. Critical Studies in Education, 56(1), 131–146. Selwyn, N., Pangrazio, L., & Cumbo, B. (2022). Knowing the (datafied) Student: The Production of the Student Subject Through School Data. British Journal of Educational Studies, 70(3), 345–361.
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